I identify a market power effect of free shipping in differentiated markets where sellers compete across varieties and locations: free shipping means uniform delivered prices and hence price discrimination in favor of distant, more costly buyers, which weakens sellers’ incentives to cut prices and leads to higher prices in equilibrium. I study this effect in the case of Integrated Free Shipping (IFS), where a retail platform commits to free shipping for buyers, integrates into logistics, and ties shipping with its intermediation service. Platforms disclose information to buyers and thereby enhance competition between sellers. As IFS softens this competition, it helps a platform attract sellers. If integration is necessary to implement this effect, IFS forecloses entry by non-integrated platforms, grants the integrated one ex ante monopoly power, and leads to higher fees and higher retail prices (for any fee). In the baseline model with a potential entrant IFS reduces welfare by increasing costs and prices. Under bilateral monopoly the welfare effect depends on the balance of double marginalization between shipping and retail. The results reveal a novel property of integration and tying that has gone unnoticed in the debate on Amazon and digital platforms more generally.
This Special Issue of the Italian Economic Journal, organized in collaboration with the Italian Association of Environmental and Resource Economists (IAERE), brings together a set of contributions that advance the economic analysis of natural hazards and human disasters. The papers share a common starting point: extreme events represent catastrophic risks characterized by deep uncertainty, potential threshold effects, and complex behavioural and institutional responses. The contributions collectively address three interconnected dimensions. First, they examine the structure of uncertainty and the cognitive foundations of catastrophic risk perception. Second, they analyse the design and effectiveness of mitigation, adaptation, and financial protection instruments. Third, they investigate the heterogeneous impacts of extreme events on agricultural productivity, territorial vulnerability, and social capital formation. Taken together, the findings highlight that planning horizons and information structures are not neutral in the presence of threshold uncertainty; that collective risk framing can systematically distort willingness to pay, with important implications for insurance market design; and that disaster impacts are asymmetric across outcomes and non-monotonic with respect to the frequency and timing of events. These insights contribute to a deeper understanding of catastrophic risk and provide relevant guidance for disaster risk governance and climate adaptation policy.
This paper describes what Extreme Event Impact Attribution (EEIA) is, what it can provide, and why it is becoming increasingly more policy relevant. The core idea of EEIA is simple: If anthropogenic climate change modifies the probability or intensity of an extreme weather event or a class of such events, and if these events caused measurable adverse impact, then some portion of the measured impacts can be attributed to climate change. In practice, this requires a clear attribution question, a counterfactual climate, an impact model or impact data, and transparent assumptions about what is held constant. The literature shows that EEIA is already useful for estimating national and global climate-attributable losses, projecting future costs, identifying inequities, measuring response costs, tracing indirect losses, valuing health and mortality impacts, informing the Fund for Responding to Loss and Damage, supporting liability claims, and communicating the costs of climate change. Data gaps, methodological differences, ethical disagreements, and limited research capacity mean that impact attribution is best understood as one component of a broader evidence base, but one which, if it develops further, can contribute a lot to our knowledge of climate change.
We investigate the interplay of search and switching costs in determining suboptimal market outcomes. Relying on the theoretical framework provided by Wilson (2012), we test whether search or switching costs have a greater detrimental impact on competition and on the mechanisms behind it. Our results show that market outcomes crucially depend on buyers’ strategy, and that, unlike theoretical predictions, switching costs may impact prices and welfare more than search costs under some circumstances.
Moving beyond the traditional focus on geographical proximity, this study investigates the existence and nature of spillover effects within a non-geographical space. The core novelty of the paper is operational and methodological: we exploit text data scraped from corporate websites to construct a novel semantic proximity matrix within a spatial econometric framework, capturing firms’ industrial specialisations, technological adoptions, and cognitive competencies. Focusing on the tech ecosystem of Milan, the study examines how the balance between positive and negative externalities shifts across different phases of firm growth. From a conceptual perspective, the paper integrates growth with spillover dynamics, explaining the mechanisms through which firm maturity transforms knowledge-sharing opportunities into competitive frictions. We distinguish between two archetypes: younger, smaller startups operating within an exploratory innovation paradigm, and more mature scaleups driven by market exploitation and asset accumulation. The empirical results reconcile these perspectives by showing that the net effect of proximity is strictly contingent on the growth stage of neighbouring firms. Proximity to startups is associated with significant positive knowledge spillovers, consistent with their role as open, learning-oriented nodes. By contrast, proximity to scaleups triggers negative competitive pressures and “businessstealing” effects, as their aggressive market expansion imposes costs on technologically and industrially close neighbours. These findings offer fresh insights for researchers and policymakers, demonstrating that in modern tech ecosystems, the impact of proximity depends entirely on who is proximate.
